OpenRaiser/Intern-Atlas huggingface dataset:
A Resource-Efficient Embedded Iris Recognition System Using Fully Convolutional Networks
Venue: arXiv cs.NE 2019
Year: 2019

Applications of Fully Convolutional Networks (FCN) in iris segmentation have shown promising advances. For mobile and embedded systems, a significant challenge is that the proposed FCN architectures are extremely computationally demanding. In this article, we propose a resource-efficient, end-to-end iris recognition flow, which consists of FCN-based segmentation, contour fitting, followed by Daugman normalization and encoding. To attain accurate and efficient FCN models, we propose a three-step SW/HW co-design methodology consisting of FCN architectural exploration, precision quantization, and hardware acceleration. In our exploration, we propose multiple FCN models, and in comparison to previous works, our best-performing model requires 50X less FLOPs per inference while achieving a new state-of-the-art segmentation accuracy. Next, we select the most efficient set of models and further reduce their computational complexity through weights and activations quantization using 8-bit dynamic fixed-point (DFP) format. Each model is then incorporated into an end-to-end flow for true recognition performance evaluation. A few of our end-to-end pipelines outperform the previous state-of-the-art on two datasets evaluated. Finally, we propose a novel DFP accelerator and fully demonstrate the SW/HW co-design realization of our flow on an embedded FPGA platform. In comparison with the embedded CPU, our hardware acceleration achieves up to 8.3X speedup for the overall pipeline while using less than 15% of the available FPGA resources. We also provide comparisons between the FPGA system and an embedded GPU showing different benefits and drawbacks for the two platforms.
TorchGAN: A Flexible Framework for GAN Training and Evaluation
Venue: arXiv cs.LG 2019
Year: 2019

TorchGAN is a PyTorch based framework for writing succinct and comprehensible code for training and evaluation of Generative Adversarial Networks. The framework's modular design allows effortless customization of the model architecture, loss functions, training paradigms, and evaluation metrics. The key features of TorchGAN are its extensibility, built-in support for a large number of popular models, losses and evaluation metrics, and zero overhead compared to vanilla PyTorch. By using the framework to implement several popular GAN models, we demonstrate its extensibility and ease of use. We also benchmark the training time of our framework for said models against the corresponding baseline PyTorch implementations and observe that TorchGAN's features bear almost zero overhead.
STA: Adversarial Attacks on Siamese Trackers
Venue: arXiv cs.CV 2019
Year: 2019

Recently, the majority of visual trackers adopt Convolutional Neural Network (CNN) as their backbone to achieve high tracking accuracy. However, less attention has been paid to the potential adversarial threats brought by CNN, including Siamese network. In this paper, we first analyze the existing vulnerabilities in Siamese trackers and propose the requirements for a successful adversarial attack. On this basis, we formulate the adversarial generation problem and propose an end-to-end pipeline to generate a perturbed texture map for the 3D object that causes the trackers to fail. Finally, we conduct thorough experiments to verify the effectiveness of our algorithm. Experiment results show that adversarial examples generated by our algorithm can successfully lower the tracking accuracy of victim trackers and even make them drift off. To the best of our knowledge, this is the first work to generate 3D adversarial examples on visual trackers.
Target Set Selection for Conservative Populations
Venue: arXiv cs.DS 2019
Year: 2019

Let $G = (V,E)$ be a graph on $n$ vertices, where $d_v$ denotes the degree of vertex $v$, and $t_v$ is a threshold associated with $v$. We consider a process in which initially a set $S$ of vertices becomes active, and thereafter, in discrete time steps, every vertex $v$ that has at least $t_v$ active neighbors becomes active as well. The set $S$ is contagious if eventually all $V$ becomes active. The target set selection problem TSS asks for the smallest contagious set. TSS is NP-hard and moreover, notoriously difficult to approximate. In the conservative special case of TSS, $t_v > \frac{1}{2}d_v$ for every $v \in V$. In this special case, TSS can be approximated within a ratio of $O(Δ)$, where $Δ= \max_{v \in V}[d_v]$. In this work we introduce a more general class of TSS instances that we refer to as conservative on average (CoA), that satisfy the condition $\sum_{v\in V} t_v > \frac{1}{2}\sum_{v \in V} d_v$. We design approximation algorithms for some subclasses of CoA. For example, if $t_v \geq \frac{1}{2}d_v$ for every $v \in V$, we can find in polynomial time a contagious set of size $\tilde{O}\left(Δ\cdot OPT^2 \right)$, where $OPT$ is the size of a smallest contagious set in $G$. We also provide several hardness of approximation results. For example, assuming the unique games conjecture, we prove that TSS on CoA instances with $Δ\le 3$ cannot be approximated within any constant factor. We also present results concerning the fixed parameter tractability of CoA TSS instances, and approximation algorithms for a related problem, that of TSS with partial incentives.
CUDA: Contradistinguisher for Unsupervised Domain Adaptation
Venue: arXiv cs.LG 2019
Year: 2019

In this paper, we propose a simple model referred as Contradistinguisher (CTDR) for unsupervised domain adaptation whose objective is to jointly learn to contradistinguish on unlabeled target domain in a fully unsupervised manner along with prior knowledge acquired by supervised learning on an entirely different domain. Most recent works in domain adaptation rely on an indirect way of first aligning the source and target domain distributions and then learn a classifier on a labeled source domain to classify target domain. This approach of an indirect way of addressing the real task of unlabeled target domain classification has three main drawbacks. (i) The sub-task of obtaining a perfect alignment of the domain in itself might be impossible due to large domain shift (e.g., language domains). (ii) The use of multiple classifiers to align the distributions unnecessarily increases the complexity of the neural networks leading to over-fitting in many cases. (iii) Due to distribution alignment, the domain-specific information is lost as the domains get morphed. In this work, we propose a simple and direct approach that does not require domain alignment. We jointly learn CTDR on both source and target distribution for unsupervised domain adaptation task using contradistinguish loss for the unlabeled target domain in conjunction with a supervised loss for labeled source domain. Our experiments show that avoiding domain alignment by directly addressing the task of unlabeled target domain classification using CTDR achieves state-of-the-art results on eight visual and four language benchmark domain adaptation datasets.
Convex Set Disjointness, Distributed Learning of Halfspaces, and LP Feasibility
Venue: arXiv cs.DS 2019
Year: 2019

We study the Convex Set Disjointness (CSD) problem, where two players have input sets taken from an arbitrary fixed domain~$U\subseteq \mathbb{R}^d$ of size $\lvert U\rvert = n$. Their mutual goal is to decide using minimum communication whether the convex hulls of their sets intersect (equivalently, whether their sets can be separated by a hyperplane). Different forms of this problem naturally arise in distributed learning and optimization: it is equivalent to {\em Distributed Linear Program (LP) Feasibility} -- a basic task in distributed optimization, and it is tightly linked to {\it Distributed Learning of Halfdpaces in $\mathbb{R}^d$}. In {communication complexity theory}, CSD can be viewed as a geometric interpolation between the classical problems of {Set Disjointness} (when~$d\geq n-1$) and {Greater-Than} (when $d=1$). We establish a nearly tight bound of $\tilde Θ(d\log n)$ on the communication complexity of learning halfspaces in $\mathbb{R}^d$. For Convex Set Disjointness (and the equivalent task of distributed LP feasibility) we derive upper and lower bounds of $\tilde O(d^2\log n)$ and~$Ω(d\log n)$. These results improve upon several previous works in distributed learning and optimization. Unlike typical works in communication complexity, the main technical contribution of this work lies in the upper bounds. In particular, our protocols are based on a {\it Container Lemma for Halfspaces} and on two variants of {\it Carathéodory's Theorem}, which may be of independent interest. These geometric statements are used by our protocols to provide a compressed summary of the players' input.
Zero-Calibration Device-free Localization for the IoT based on Participatory Sensing
Venue: arXiv cs.NI 2019
Year: 2019

Device-free localization (DFL) is an emerging technology for estimating the position of a human or object that is not equipped with any electronic tag, nor participate actively in the localization process. Similar to device-based localization, the initial phase in DFL is to build the fingerprint database which is usually done manually using site surveying. This process is tedious, time-consuming, and vulnerable to environmental dynamics. Motivated by the recent advances in the Internet of Things (IoT), this paper introduces RadioGrapher; a system that automates the process of device-free fingerprint calibration in IoT environments. RadioGrapher leverages the device-based locations of entities in the area of interest in a crowd-sensing manner, aided with Fresnel zones of the wirelessly connected IoT devices to automatically construct a device-free fingerprint. Experimental evaluation of RadioGrapher in an IoT testbed using multiple entities shows that it can construct DFL fingerprints with high accuracy. Moreover, its median localization accuracy is comparable to that of manual fingerprinting. This comes with no calibration overhead, highlighting the promise of RadioGrapher as a crowdsourcing device-free fingerprint constructor in IoT environments.
AtLoc: Attention Guided Camera Localization
Venue: arXiv cs.CV 2019
Year: 2019

Deep learning has achieved impressive results in camera localization, but current single-image techniques typically suffer from a lack of robustness, leading to large outliers. To some extent, this has been tackled by sequential (multi-images) or geometry constraint approaches, which can learn to reject dynamic objects and illumination conditions to achieve better performance. In this work, we show that attention can be used to force the network to focus on more geometrically robust objects and features, achieving state-of-the-art performance in common benchmark, even if using only a single image as input. Extensive experimental evidence is provided through public indoor and outdoor datasets. Through visualization of the saliency maps, we demonstrate how the network learns to reject dynamic objects, yielding superior global camera pose regression performance. The source code is avaliable at https://github.com/BingCS/AtLoc.
Lessons Learned from Developing a Microservice Based Mobile Location-Based Crowdsourcing Platform
Venue: arXiv cs.HC 2019
Year: 2019

Research in Mobile Location-Based Crowdsourcing is hindered by a marked lack of real-world data. The development of a standardized, lightweight, easily deployable, modular, composable, and most of all, scalable experimentation framework would go a long way in facilitating such research. Conveniently, these are all salient characteristics of systems developed using a microservices approach. We propose QRowdsource - a MLBC experimentation framework built using a distributed services architecture. In this paper, we discuss the design and development of QRowdsource, from the decomposition of functional components to the orchestration of services within the framework. We also take a look at how the advantages and disadvantages of using a microservices approach translate to our specific use case and deliberate over a number of lessons learned while developing the experimentation framework.
DublinCity: Annotated LiDAR Point Cloud and its Applications
Venue: arXiv cs.CV 2019
Year: 2019

Scene understanding of full-scale 3D models of an urban area remains a challenging task. While advanced computer vision techniques offer cost-effective approaches to analyse 3D urban elements, a precise and densely labelled dataset is quintessential. The paper presents the first-ever labelled dataset for a highly dense Aerial Laser Scanning (ALS) point cloud at city-scale. This work introduces a novel benchmark dataset that includes a manually annotated point cloud for over 260 million laser scanning points into 100'000 (approx.) assets from Dublin LiDAR point cloud [12] in 2015. Objects are labelled into 13 classes using hierarchical levels of detail from large (i.e., building, vegetation and ground) to refined (i.e., window, door and tree) elements. To validate the performance of our dataset, two different applications are showcased. Firstly, the labelled point cloud is employed for training Convolutional Neural Networks (CNNs) to classify urban elements. The dataset is tested on the well-known state-of-the-art CNNs (i.e., PointNet, PointNet++ and So-Net). Secondly, the complete ALS dataset is applied as detailed ground truth for city-scale image-based 3D reconstruction.
A Stochastic Quasi-Newton Method with Nesterov's Accelerated Gradient
Venue: arXiv cs.LG 2019
Year: 2019

Incorporating second order curvature information in gradient based methods have shown to improve convergence drastically despite its computational intensity. In this paper, we propose a stochastic (online) quasi-Newton method with Nesterov's accelerated gradient in both its full and limited memory forms for solving large scale non-convex optimization problems in neural networks. The performance of the proposed algorithm is evaluated in Tensorflow on benchmark classification and regression problems. The results show improved performance compared to the classical second order oBFGS and oLBFGS methods and popular first order stochastic methods such as SGD and Adam. The performance with different momentum rates and batch sizes have also been illustrated.
Machine learning for automatic construction of pseudo-realistic pediatric abdominal phantoms
Venue: arXiv cs.LG 2019
Year: 2019

Machine Learning (ML) is proving extremely beneficial in many healthcare applications. In pediatric oncology, retrospective studies that investigate the relationship between treatment and late adverse effects still rely on simple heuristics. To assess the effects of radiation therapy, treatment plans are typically simulated on phantoms, i.e., virtual surrogates of patient anatomy. Currently, phantoms are built according to reasonable, yet simple, human-designed criteria. This often results in a lack of individualization. We present a novel approach that combines imaging and ML to build individualized phantoms automatically. Given the features of a patient treated historically (only 2D radiographs available), and a database of 3D Computed Tomography (CT) imaging with organ segmentations and relative patient features, our approach uses ML to predict how to assemble a patient-specific phantom automatically. Experiments on 60 abdominal CTs of pediatric patients show that our approach constructs significantly more representative phantoms than using current phantom building criteria, in terms of location and shape of the abdomen and of two considered organs, the liver and the spleen. Among several ML algorithms considered, the Gene-pool Optimal Mixing Evolutionary Algorithm for Genetic Programming (GP-GOMEA) is found to deliver the best performing models, which are, moreover, transparent and interpretable mathematical expressions.
Proconda -- Protected Control Data
Venue: arXiv cs.CR 2019
Year: 2019

Memory corruption vulnerabilities often enable attackers to take control of a target system by overwriting control-flow relevant data (such as return addresses and function pointers), which are potentially stored in close proximity of related, typically user-controlled data on the stack. In this paper, we propose ProConDa, a general approach for protecting control-flow relevant data on the stack ProConDa leverages hardware features to enforce a strict separation between control-flow relevant and regular data of programs written in non-memory-safe languages such as C. Contrary to related approaches, ProConDa does not rely on information hiding and is therefore not susceptible to several recent attacks specifically targeting information hiding as a foundation for memory isolation. We show that ProConDa enforcement is compatible with existing software by applying a software-based prototype to industry benchmarks on an ARM CPU running Linux.
2-Local Hamiltonian with Low Complexity is QCMA
Venue: arXiv cs.CC 2019
Year: 2019

We prove that 2-Local Hamiltonian (2-LH) with Low Complexity problem is QCMA-complete by combining the results from the QMA-completeness[4] of 2-LH and QCMA-completeness of 3-LH with Low Complexity[6]. The idea is straightforward. It has been known that 2-LH is QMA-complete. By putting a low complexity constraint on the input state, we make the problem QCMA. Finally, we use similar arguments as in [4] to show that all QCMA problems can be reduced to our proposed problem.
Subjectivity Learning Theory towards Artificial General Intelligence
Venue: arXiv cs.AI 2019
Year: 2019

The construction of artificial general intelligence (AGI) was a long-term goal of AI research aiming to deal with the complex data in the real world and make reasonable judgments in various cases like a human. However, the current AI creations, referred to as "Narrow AI", are limited to a specific problem. The constraints come from two basic assumptions of data, which are independent and identical distributed samples and single-valued mapping between inputs and outputs. We completely break these constraints and develop the subjectivity learning theory for general intelligence. We assign the mathematical meaning for the philosophical concept of subjectivity and build the data representation of general intelligence. Under the subjectivity representation, then the global risk is constructed as the new learning goal. We prove that subjectivity learning holds a lower risk bound than traditional machine learning. Moreover, we propose the principle of empirical global risk minimization (EGRM) as the subjectivity learning process in practice, establish the condition of consistency, and present triple variables for controlling the total risk bound. The subjectivity learning is a novel learning theory for unconstrained real data and provides a path to develop AGI.
Efficient Automatic Meta Optimization Search for Few-Shot Learning
Venue: arXiv cs.LG 2019
Year: 2019

Previous works on meta-learning either relied on elaborately hand-designed network structures or adopted specialized learning rules to a particular domain. We propose a universal framework to optimize the meta-learning process automatically by adopting neural architecture search technique (NAS). NAS automatically generates and evaluates meta-learner's architecture for few-shot learning problems, while the meta-learner uses meta-learning algorithm to optimize its parameters based on the distribution of learning tasks. Parameter sharing and experience replay are adopted to accelerate the architectures searching process, so it takes only 1-2 GPU days to find good architectures. Extensive experiments on Mini-ImageNet and Omniglot show that our algorithm excels in few-shot learning tasks. The best architecture found on Mini-ImageNet achieves competitive results when transferred to Omniglot, which shows the high transferability of architectures among different computer vision problems.
SRIM and SCRIM Factors of $x^n+1$ over Finite Fields and Their Applications
Venue: arXiv cs.IT 2019
Year: 2019

Self-Reciprocal Irreducible Monic (SRIM) and Self-Conjugate-Reciprocal Irreducible Monic (SRCIM) factors of $x^n-1$ over finite fields have become of interest due to their rich algebraic structures and wide applications. In this paper, these notions are extended to factors of $x^n+ 1$ over finite fields. Characterization and enumeration of SRIM and SCRIM factors of $x^n+1$ over finite fields are established. Simplification and recessive formulas for the number of such factors are given. Finally, applications in the studied of complementary negacyclic codes are discussed.
Aligning Daily Activities with Personality: Towards A Recommender System for Improving Wellbeing
Venue: arXiv cs.HC 2019
Year: 2019

Recommender Systems have not been explored to a great extent for improving health and subjective wellbeing. Recent advances in mobile technologies and user modelling present the opportunity for delivering such systems, however the key issue is understanding the drivers of subjective wellbeing at an individual level. In this paper we propose a novel approach for deriving personalized activity recommendations to improve subjective wellbeing by maximizing the congruence between activities and personality traits. To evaluate the model, we leveraged a rich dataset collected in a smartphone study, which contains three weeks of daily activity probes, the Big-Five personality questionnaire and subjective wellbeing surveys. We show that the model correctly infers a range of activities that are 'good' or 'bad' (i.e. that are positively or negatively related to subjective wellbeing) for a given user and that the derived recommendations greatly match outcomes in the real-world.
Distributed creation of Machine learning agents for Blockchain analysis
Venue: arXiv cs.LG 2019
Year: 2019

Creating efficient deep neural networks involves repetitive manual optimization of the topology and the hyperparameters. This human intervention significantly inhibits the process. Recent publications propose various Neural Architecture Search (NAS) algorithms that automate this work. We have applied a customized NAS algorithm with network morphism and Bayesian optimization to the problem of cryptocurrency predictions, where it achieved results on par with our best manually designed models. This is consistent with the findings of other teams, while several known experiments suggest that given enough computing power, NAS algorithms can surpass state-of-the-art neural network models designed by humans. In this paper, we propose a blockchain network protocol that incentivises independent computing nodes to run NAS algorithms and compete in finding better neural network models for a particular task. If implemented, such network can be an autonomous and self-improving source of machine learning models, significantly boosting and democratizing the access to AI capabilities for many industries.
Do you really follow them? Automatic detection of credulous Twitter users
Venue: arXiv cs.SI 2019
Year: 2019

Online Social Media represent a pervasive source of information able to reach a huge audience. Sadly, recent studies show how online social bots (automated, often malicious accounts, populating social networks and mimicking genuine users) are able to amplify the dissemination of (fake) information by orders of magnitude. Using Twitter as a benchmark, in this work we focus on what we define credulous users, i.e., human-operated accounts with a high percentage of bots among their followings. Being more exposed to the harmful activities of social bots, credulous users may run the risk of being more influenced than other users; even worse, although unknowingly, they could become spreaders of misleading information (e.g., by retweeting bots). We design and develop a supervised classifier to automatically recognize credulous users. The best tested configuration achieves an accuracy of 93.27% and AUC-ROC of 0.93, thus leading to positive and encouraging results.